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DRAM has scaled to achieve low cost per bit and this scaling has decreasedRowhammer threshold. Thus, DRAM has adopted Target-Row-Refresh (TRR) whichrefreshes victim rows that can possibly lose stored data due to neighboringaggressor rows. Accordingly, prior works focused on TRR algorithm that canidentify the most frequently accessed row, Rowhammer, to detect the victimrows. TRR algorithm can be implemented in memory controller, yet it can causeeither insufficient or excessive number of TRRs due to lack of information ofRowhammer threshold and it can also degrade system performance due toadditional command for Rowhammer mitigation. Therefore, this paper focuses onin-DRAM TRR algorithm.  In general, counter-based detection algorithms have higher detection accuracyand scalability compared to probabilistic detection algorithms. However, modernDRAM extremely limits the number of counters for TRR algorithm. This paperdemonstrates that decoy-rows are the fundamental reason the state-of-the-artcounter-based algorithms cannot properly detect Rowhammer under the arealimitation. Decoy-rows are rows whose number of accesses does not exceed thenumber of Rowhammer accesses within an observation period. Thus, decoy-rowsshould not replace Rowhammer which are in a count table. Unfortunately, none ofthe state-of-the-art counter-based algorithms filter out decoy-rows.Consequently, decoy-rows replace Rowhammer in a count table and dispossess TRRopportunities from victim rows. Therefore, this paper proposes `in-DRAMStochastic and Approximate Counting (DSAC) algorithm', which leveragesStochastic Replacement to filter out decoy-rows and Approximate Counting forlow area cost. The key idea is that a replacement occurs if a new row comes inmore than a minimum count row in a count table on average so that decoy-rowscannot replace Rowhammer in a count table.

DSAC: Low-Cost Rowhammer Mitigation Using In-DRAM Stochastic and Approximate Counting Algorithm